Clark David E, Black Adam W, Skavdahl David H, Hallagan Lee D
Department of Surgery, Maine Medical Center, Portland, ME, USA.
MMC Center for Outcomes Research and Evaluation, Maine Medical Center, 509 Forest Avenue, Portland, ME, 04101, USA.
Inj Epidemiol. 2018 Apr 9;5(1):11. doi: 10.1186/s40621-018-0149-8.
The article introduces Programs for Injury Categorization, using the International Classification of Diseases (ICD) and R statistical software (ICDPIC-R). Starting with ICD-8, methods have been described to map injury diagnosis codes to severity scores, especially the Abbreviated Injury Scale (AIS) and Injury Severity Score (ISS). ICDPIC was originally developed for this purpose using Stata, and ICDPIC-R is an open-access update that accepts both ICD-9 and ICD-10 codes.
Data were obtained from the National Trauma Data Bank (NTDB), Admission Year 2015. ICDPIC-R derives CDC injury mechanism categories and an approximate ISS ("RISS") from either ICD-9 or ICD-10 codes. For ICD-9-coded cases, RISS is derived similar to the Stata package (with some improvements reflecting user feedback). For ICD-10-coded cases, RISS may be calculated in several ways: The "GEM" methods convert ICD-10 to ICD-9 (using General Equivalence Mapping tables from CMS) and then calculate ISS with options similar to the Stata package; a "ROCmax" method calculates RISS directly from ICD-10 codes, based on diagnosis-specific mortality in the NTDB, maximizing the C-statistic for predicting NTDB mortality while attempting to minimize the difference between RISS and ISS submitted by NTDB registrars (ISSAIS). Findings were validated using data from the National Inpatient Survey (NIS, 2015).
NTDB contained 917,865 cases, of which 86,878 had valid ICD-10 injury codes. For a random 100,000 ICD-9-coded cases in NTDB, RISS using the GEM methods was nearly identical to ISS calculated by the Stata version, which has been previously validated. For ICD-10-coded cases in NTDB, categorized ISS using any version of RISS was similar to ISSAIS; for both NTDB and NIS cases, increasing ISS was associated with increasing mortality. Prediction of NTDB mortality was associated with C-statistics of 0.81 for ISSAIS, 0.75 for RISS using the GEM methods, and 0.85 for RISS using the ROCmax method; prediction of NIS mortality was associated with C-statistics of 0.75-0.76 for RISS using the GEM methods, and 0.78 for RISS using the ROCmax method. Instructions are provided for accessing ICDPIC-R at no cost.
The ideal methods of injury categorization and injury severity scoring involve trained personnel with access to injured persons or their medical records. ICDPIC-R may be a useful substitute when this ideal cannot be obtained.
本文介绍了使用国际疾病分类法（ICD）和R统计软件（ICDPIC-R）进行损伤分类的程序。从ICD-8开始，已有方法可将损伤诊断编码映射为严重程度评分，特别是简明损伤定级（AIS）和损伤严重度评分（ISS）。ICDPIC最初是为此目的使用Stata开发的，而ICDPIC-R是一个开放获取的更新版本，它同时接受ICD-9和ICD-10编码。
数据来自国家创伤数据库（NTDB），收录年份为2015年。ICDPIC-R可从ICD-9或ICD-10编码中得出美国疾病控制与预防中心（CDC）的损伤机制类别和一个近似的ISS（“RISS”）。对于ICD-9编码的病例，RISS的推导方式与Stata软件包类似（有一些改进以反映用户反馈）。对于ICD-10编码的病例，RISS可以通过几种方式计算：“GEM”方法将ICD-10转换为ICD-9（使用医疗保险和医疗补助服务中心（CMS）的通用等效映射表），然后使用与Stata软件包类似的选项计算ISS；“ROCmax”方法直接根据NTDB中特定诊断的死亡率从ICD-10编码计算RISS，在尝试最小化RISS与NTDB登记员提交的ISS（ISSAIS）之间差异的同时，最大化预测NTDB死亡率的C统计量。研究结果使用来自国家住院病人调查（NIS，2015年）的数据进行了验证。
NTDB包含917,
